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Related Experiment Videos

A ChIP-Seq Data Analysis Pipeline Based on Bioconductor Packages.

Seung-Jin Park1,2, Jong-Hwan Kim1,2, Byung-Ha Yoon1,2

  • 1Personalized Genomic Medicine Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Korea.

Genomics & Informatics
|April 19, 2017
PubMed
Summary

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This study presents a streamlined chromatin immunoprecipitation-sequencing (ChIP-Seq) data analysis workflow using four Bioconductor packages. The workflow efficiently processes sequencing data for DNA-protein interaction studies across all operating systems.

Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Vast amounts of chromatin immunoprecipitation-sequencing (ChIP-Seq) data are generated to understand DNA-protein interactions.
  • Numerous computational tools exist for ChIP-Seq data analysis, necessitating efficient and integrated solutions.

Purpose of the Study:

  • To present a streamlined and integrated workflow for ChIP-Seq data analysis.
  • To demonstrate the utility of combining dada2, QuasR, mosaics, and ChIPseeker packages for comprehensive ChIP-Seq analysis.

Main Methods:

  • Utilized dada2 for high-throughput sequencing data trimming.
  • Employed QuasR for quality control and read mapping to the reference genome.
  • Applied mosaics for peak calling and ChIPseeker for peak annotation and visualization.
Keywords:
chromatin immunoprecipitationdata analysisnext-generation sequencingstatistical

Related Experiment Videos

  • Integrated these four Bioconductor packages into a single, efficient workflow.
  • Main Results:

    • The workflow successfully processes raw fastq files into annotated peaks in a single run.
    • The presented ChIP-Seq analysis workflow is compatible with multiple operating systems (Windows, Mac, Linux).
    • The R code for the workflow is publicly available on GitHub.

    Conclusions:

    • This integrated workflow offers an efficient and user-friendly approach to ChIP-Seq data analysis.
    • The streamlined process facilitates the study of DNA-protein interactions from raw sequencing data to biological insights.
    • The workflow's cross-platform compatibility enhances its accessibility for researchers worldwide.